AssertionError:某些对象具有未还原的属性 [英] AssertionError: Some objects had attributes which were not restored

问题描述

我正在通过TensorFlow官方网站在这里训练有关文本预测的基本LSTM.>.我设法在GTX 1050ti上训练了多达40个时期的模型，并将checkPoint文件保存在一个单独的文件夹中.但是，当我现在尝试还原模型时，出现此长错误:-

I was training a basic LSTM on text prediction by following the official TensorFlow site here. I had managed to train my model up to 40 epochs on a GTX 1050ti and had saved the checkPoint file in a separate folder. However, when I now try to restore the model, I get this long error:-

StreamExecutor device (0): GeForce GTX 1050 Ti, Compute Capability 6.1
WARNING:tensorflow:Entity <function standard_gru at 0x7f9e121324d0> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <function standard_gru at 0x7f9e121324d0>: AttributeError: module 'gast' has no attribute 'Num'
WARNING:tensorflow:Entity <function cudnn_gru at 0x7f9e120c1d40> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <function cudnn_gru at 0x7f9e120c1d40>: AttributeError: module 'gast' has no attribute 'Num'
WARNING:tensorflow:Entity <function standard_gru at 0x7f9e121324d0> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <function standard_gru at 0x7f9e121324d0>: AttributeError: module 'gast' has no attribute 'Num'
WARNING:tensorflow:Entity <function cudnn_gru at 0x7f9e120c1d40> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <function cudnn_gru at 0x7f9e120c1d40>: AttributeError: module 'gast' has no attribute 'Num'
WARNING:tensorflow:From /home/awesome_ruler/.local/lib/python3.7/site-packages/tensorflow/python/training/tracking/util.py:1200: NameBasedSaverStatus.__init__ (from tensorflow.python.training.tracking.util) is deprecated and will be removed in a future version.
Instructions for updating:
Restoring a name-based tf.train.Saver checkpoint using the object-based restore API. This mode uses global names to match variables, and so is somewhat fragile. It also adds new restore ops to the graph each time it is called when graph building. Prefer re-encoding training checkpoints in the object-based format: run save() on the object-based saver (the same one this message is coming from) and use that checkpoint in the future.
Traceback (most recent call last):
  File "main.py", line 95, in <module>
    model.load_weights(checkpoint_dir)
  File "/home/awesome_ruler/.local/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py", line 162, in load_weights
    return super(Model, self).load_weights(filepath, by_name)
  File "/home/awesome_ruler/.local/lib/python3.7/site-packages/tensorflow/python/keras/engine/network.py", line 1398, in load_weights
    status.assert_nontrivial_match()
  File "/home/awesome_ruler/.local/lib/python3.7/site-packages/tensorflow/python/training/tracking/util.py", line 917, in assert_nontrivial_match
    return self.assert_consumed()
  File "/home/awesome_ruler/.local/lib/python3.7/site-packages/tensorflow/python/training/tracking/util.py", line 894, in assert_consumed
    (unused_attributes,))
AssertionError: Some objects had attributes which were not restored: {<tf.Variable 'embedding_1/embeddings:0' shape=(65, 256) dtype=float32, numpy=
array([[-0.00044268, -0.02351714, -0.01139065, ..., -0.00327835,
         0.00074228, -0.00383734],
       [-0.02313181,  0.04697707, -0.02350216, ...,  0.040385  ,
         0.03087702,  0.02765551],
       [ 0.0410727 ,  0.00130001,  0.0051438 , ...,  0.02899202,
         0.04258115, -0.03773504],
       ...,
       [-0.03134514,  0.01370119,  0.00993627, ..., -0.02257681,
         0.02617678,  0.03761976],
       [-0.02954974,  0.02407967,  0.02768463, ..., -0.0056519 ,
        -0.01507735,  0.04617763],
       [-0.04113789, -0.03544737,  0.01056757, ...,  0.01236727,
        -0.01791535, -0.01635399]], dtype=float32)>: ['embedding_1/embeddings'], <tf.Variable 'dense_1/kernel:0' shape=(1024, 65) dtype=float32, numpy=
array([[-6.7811467e-02, -2.5536597e-02,  5.1763237e-02, ...,
        -6.9665730e-02,  3.9457709e-02, -5.3290475e-02],
       [ 1.5835620e-02, -3.0763537e-02, -7.4058644e-02, ...,
         3.8087368e-05, -9.1508478e-03,  5.5485427e-02],
       [ 3.8143486e-02,  8.8131428e-04, -2.3478847e-02, ...,
        -1.5135627e-02, -5.2146181e-02,  7.1185097e-02],
       ...,
       [-6.6591002e-02,  4.7627889e-02,  5.7474524e-02, ...,
         4.1528463e-02,  4.6467118e-02, -3.0670539e-02],
       [-5.0804108e-02,  5.4505378e-02, -1.5776977e-03, ...,
         2.1875933e-02, -2.9637258e-02,  2.0201296e-02],
       [-4.7325939e-02, -8.0013275e-03, -3.6348965e-02, ...,
        -7.0560835e-02, -4.9752403e-02,  1.0509960e-02]], dtype=float32)>: ['dense_1/kernel'], <tf.Variable 'dense_1/bias:0' shape=(65,) dtype=float32, numpy=
array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
      dtype=float32)>: ['dense_1/bias'], <tf.Variable 'gru_1/kernel:0' shape=(256, 3072) dtype=float32, numpy=
array([[ 0.00432818,  0.03131782,  0.00038544, ..., -0.00559966,
         0.03458985, -0.03219106],
       [-0.00865119,  0.01648769, -0.00768028, ...,  0.01366192,
        -0.03043955, -0.01382086],
       [-0.01379537,  0.00547716, -0.00385967, ..., -0.00027269,
        -0.01285852,  0.0377048 ],
       ...,
       [-0.01940641,  0.01454895,  0.03349226, ..., -0.04234404,
        -0.02699661,  0.0376601 ],
       [ 0.00186675, -0.00547577, -0.02205843, ..., -0.01287581,
        -0.02314153,  0.04158166],
       [ 0.00954719, -0.02883693, -0.03259185, ..., -0.02587803,
         0.02906795, -0.00559821]], dtype=float32)>: ['gru_1/kernel'], <tf.Variable 'gru_1/recurrent_kernel:0' shape=(1024, 3072) dtype=float32, numpy=
array([[ 9.11542401e-03,  1.50135346e-02,  2.96630897e-02, ...,
         2.25223936e-02,  2.31253020e-02, -2.96920985e-02],
       [-2.21075956e-02, -8.46013427e-06, -2.16848943e-02, ...,
        -1.26914177e-02, -3.49153839e-02, -3.01396102e-02],
       [-3.59148793e-02,  9.98445973e-03,  2.60963626e-02, ...,
         3.15430500e-02,  1.28889643e-02,  3.37569825e-02],
       ...,
       [ 3.39106433e-02,  6.54980540e-03, -1.27352085e-02, ...,
        -4.14674729e-03,  3.53236459e-02, -1.36333425e-02],
       [-3.50691415e-02, -1.76392253e-02,  1.67468414e-02, ...,
        -2.06982102e-02, -1.06042419e-02,  2.26641595e-02],
       [-1.14825107e-02, -3.46554294e-02, -1.83847174e-03, ...,
         2.25809850e-02,  2.45791934e-02, -2.70933360e-02]], dtype=float32)>: ['gru_1/recurrent_kernel'], <tf.Variable 'gru_1/bias:0' shape=(2, 3072) dtype=float32, numpy=
array([[0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.]], dtype=float32)>: ['gru_1/bias']}

我正在尝试加载文件 ckpt_40.index ，如您所见，它是最新的检查点.但是我不能.我正在使用此代码加载我的模型==>

I am trying to load the file ckpt_40.index which, as you can see is the latest checkpoint. However I am not able to. I am using this code to load my model ==>

checkpoint_dir = 'CheckPoints/ckpt_40.index'

model = build_model(vocab_size, embedding_dim, rnn_units, batch_size=1)

model.load_weights(checkpoint_dir)

model.summary()

并且正在使用网站上的 generate_text 函数来预测某些内容.

And am using the generate_text function from the website to predict something.

我认为类似的问题已发布在此处的堆栈溢出中，但没有得到答复.我正在使用Tf [GPU] 2.0- beta1，它是GPU的最新tf版本...

I think a similar issue was posted on Stack overflow here, but it wasn't answered. I am using Tf[GPU] 2.0- beta1 which was the latest tf version for GPU...

推荐答案

我犯了一个非常愚蠢的错误，这个错误很小，以至于我怀疑有人会捡起它.在这一行:-

A really idiotic mistake I made, which was so minor that I doubt anyone could have picked it up. In this line :-

checkpoint_dir = 'CheckPoints/ckpt_40.index'

尽管文件被命名为带有'.index'前缀，但由于某种原因，将该扩展名附加到变量/调用函数后，由于某种原因，它会感到恐慌(可能是错误).本来会更有用的是指出错误的扩展名错误.

Though the file is named to have the '.index' prefix, For some reason attaching that extension to the variable/call function has caused it to panic for some reason(Maybe a bug). What would have been more helpful was an error to point out the incorrect extension.

对于其他有此问题的人，只需将您的检查点目录更改为===>

So for anyone else having this problem, just change your checkpoint directory to this ===>

checkpoint_dir = 'CheckPoints/ckpt_40  # .index has been removed'

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